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In laser welding, precision and reproducibility are fundamentally dependent on temporal and spatial processes of energy input. Induced by the dynamics of the melt pool, pressure equilibria in the vapor capillary, and solidification behavior, different weld seam qualities are achieved. To obtain the lowest possible defect frequency, new tailored joining strategies need to be investigated using multibeam and multiwavelength approaches. To improve the quality by influencing the process dynamics, a dual-beam approach is investigated that superimposes a stationary laser beam with a wavelength of 445 nm with a spatially modulated laser beam with a wavelength of 1070 nm. The aim is to utilize ∼10 times higher absorption of a 445 nm diode laser on copper with the high focusability of a 1070 nm fiber laser. In this context, the influence of the relative positions of the two beams to each other on the weld seam quality is investigated, while one of the beams moves either in front, behind, or coaxial to the other beam following the path of a line weld. The main objective is to observe how the laser beams influence each other and how the capillary depth and porosity vary for different parameters. To visualize the process dynamics, the welding experiments on copper are performed at the Deutsches Elektronen-Synchrotron DESY by means of in situ phase contrast videography. Quality-determining weld properties like the distribution of pores or process fluctuations are then extracted automatically from the image sequences by means of a trained neuronal network. [ABSTRACT FROM AUTHOR]

Disturbance factors, such as self-excited tool vibrations, limit the performance of modern machining processes and thus restrict the quality, productivity and sustainability of industrially manufactured components. The dynamic process stability is subjected to significant variances especially at the beginning of the tool life. To precisely quantify these variances, series of milling tests were conducted and analyzed using tools made of high speed steel (HSS) and cemented carbide for the machining of EN AW-7075. In all test series, a critical initial decrease of the stability limit was detected directly at the beginning of the tool life. In the following, a significant increase and subsequent almost constant stability limit was observed. In the case of the HSS cutters the achieved stability limit exceeded the initial level substantially. An increase in the process forces and flank wear was also measured over tool life but the progression cannot directly explain the extend and characteristics regarding the stability limit. The findings described have a considerable impact on the appropriate design of manufacturing processes and experiments to determine the dynamic stability of cutting operations. [ABSTRACT FROM AUTHOR]

Process mining is advancing as a powerful tool for revealing valuable insights about process dynamics. Nevertheless, the imperative to employ process mining to enhance process transparency is a prevailing concern for organizations. Despite the widespread desire to integrate process mining as a pivotal catalyst for fostering a more agile and flexible Business Process Management (BPM) environment, many organizations face challenges in achieving widespread implementation and adoption due to deficiencies in various dimensions of process mining readiness. The current Information Systems (IS) knowledge base lacks a comprehensive framework to aid organizations in augmenting their process mining readiness and bridging this intention-action gap. The paper presents a Process Mining Maturity Model (P3M), refined through multiple iterations, which outlines five factors and 23 elements that organizations must address to increase their process mining readiness. The maturity model advances the understanding of how to close the intention-action gap of process mining initiatives in multiple dimensions. Furthermore, insights from a comprehensive analysis of data gathered in eleven qualitative interviews are drawn, elucidating 30 possible actions that organizations can implement to establish a more responsive and dynamic BPM environment by means of process mining. [ABSTRACT FROM AUTHOR]

In milling processes in which material removal is performed periodically from solid material, dynamic effects are generally considered to be responsible for instabilities and subsequent productivity limits. Usually, in such applications, the process-inherent complex dynamic load spectrum on machines, tools and workpieces is considered together with vibration-based relative displacements that can be attributed to the regenerative effect. There are numerous techniques in the literature addressing the suppression of these dynamic effects, but they require a large amount of analysis and implementation effort as well as specific expert knowledge. The approach presented here, however, provides a universally applicable method for suppressing chatter vibrations and deflections. By applying structure elements to the flanks of the minor cutting edges of HSS end mills, it was possible to increase the chatter-free limiting depth of cut ap,crit in the milling processes of the aluminum alloy EN AW-7075. Structured tools were used in ramp milling tests to investigate various effects, such as the influence of certain geometric design features on the stabilization potential compared to a reference tool. Furthermore, the effects of varied process parameter configurations and wear-related effects on the performance of the tool concept were focused on as well. The three key design features of the cutting edge and the structured profiles were identified from the results of the investigation, which, when combined in the most efficient design, in each case led to the development of an optimized structure and process configuration with cumulative potential for increasing the stability limit up to 200%. [ABSTRACT FROM AUTHOR]

In many contexts of multilingualism, language learners can initiate communication in the target language (TL), or a contact language (such as English). Patterns of use emerging from these choices vary between individuals and affect TL development. Willingness to communicate (WTC) needs to be investigated in ways that capture these variations. So far, WTC has not been studied in multilingual contexts, or using individual-level designs. This case study explores intraindividual variability in the WTC propensities of adult learners of Swedish for whom the TL and English provide viable communication options in community interaction. Carried out over a period where TL skills began to develop, the purpose was to explore the process characteristics of changes in communication-initiation propensities. A person-context dynamics perspective was employed, and analyses of time-serial data were combined with analyses of concurrently generated interview data. Results reveal how changes in WTC could be gradual and nongradual, continuous and discontinuous. [ABSTRACT FROM AUTHOR]

In many contexts of multilingualism, language learners can initiate communication in the target language (TL), or a contact language (such as English). Patterns of use emerging from these choices affect TL development. They also vary between individuals. Willingness to communicate (WTC) needs to be investigated in ways that capture these variations. So far, WTC has not been studied in multilingual contexts, or using individual-level longitudinal designs. Employing a single-case, time-serial design and focused on a critical period of TL growth, this study explores WTC trajectories of adult learners of Swedish for whom the TL and English provide viable communication options in community interaction. Change point and moving window correlational analyses reveal the operation of mutually interacting influences that shape WTC and have system-level effects. With light shed on processes at the developmental timescale, findings are discussed in the context of language choice, co-evolution, and the trait–state dichotomy. [ABSTRACT FROM AUTHOR]

Laser beam welding as a reliable tool for high-precision joining of batteries or microelectronics is more and more the choice for achieving reproducible results in production processes. In addition to a high automation capability, the precise control of the energy deposition into the material plays an important role, especially when highly reflective materials, such as copper or aluminum, must be welded together. Alongside the use of highly brilliant fiber lasers in the near-infrared range with a focal diameter of a few tens of micrometers, diode lasers in the wavelength range of 445 nm are increasingly being used. Here, beam diameters of a few hundred micrometers can be achieved. With a wavelength of 445 nm, the absorptivity in copper can be increased by more than a factor of 10 compared to a near-infrared laser beam sources in solid state at room temperature. This paper presents the in situ X-ray observation of laser welding processes on CuSn6 with a laser beam source with a wavelength of 445 nm using synchrotron radiation at DESY Petra III Beamline P07 EH4 in Hamburg, Germany. For the experiments, the laser radiation was focused via two separate optics to focal diameters of 362 µm and 609 µm. To characterize the dynamics of the vapor capillaries depending on the different focal diameters dF, the parameters were varied with respect to laser power PL and feed rate v. For the investigations, a synchrotron beam of 2 × 2 mm2 in size with a photon energy of 89 keV was used, and the material samples were analyzed by means of phase-contrast videography to show the boundaries between solid, liquid, and gaseous material phases. The results of this paper show the welding depths achieved and how the geometry of the vapor capillary behaves by changing the focal diameter, laser power and feed rate. [ABSTRACT FROM AUTHOR]

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Several mental illnesses, characterized by aberrant stress reactivity, often arise after early-life adversity (ELA). However, it is unclear how ELA affects stress-related brain circuit maturation, provoking these enduring vulnerabilities. We find that ELA increases functional excitatory synapses onto stress-sensitive hypothalamic corticotropin-releasing hormone (CRH)-expressing neurons, resulting from disrupted developmental synapse pruning by adjacent microglia. Microglial process dynamics and synaptic element engulfment were attenuated in ELA mice, associated with deficient signaling of the microglial phagocytic receptor MerTK. Accordingly, selective chronic chemogenetic activation of ELA microglia increased microglial process dynamics and reduced excitatory synapse density to control levels. Notably, selective early-life activation of ELA microglia normalized adult acute and chronic stress responses, including stress-induced hormone secretion and behavioral threat responses, as well as chronic adrenal hypertrophy of ELA mice. Thus, microglial actions during development are powerful contributors to mechanisms by which ELA sculpts the connectivity of stress-regulating neurons, promoting vulnerability to stress and stress-related mental illnesses.

De-coring is an essential process in the casting process chain, determining the quality and cost of production. In this study, a coupled multibody system (MBS) and finite element modeling (FEM) technique is presented to study the mechanical loads during the de-coring process. The removal of cast-in sand cores from the inner regions of the cast part by de-coring or knocking out is a complex process with dynamic loads. Currently, the process relies upon empirical knowledge and tests. Inorganic sand cores pose additional challenges in the success of the de-coring process. Increasing complexity in geometry and stringent environmental regulations compel a predictive process in the earlier stages of design. Predicting the process' success is challenged by the dynamic non-linearities of the system. The dynamic characteristics and the interaction between hammer and casting were studied here for the first time using an industrial-based test rig, and a novel modeling approach was formulated. The results of the developed model are in good compliance with the experiments. The methodology presented in this study can be used to include a varying number of hammers and loads. The proposed approach presents the possibility to discretize the process and qualitatively assess the process parameters for optimization. [ABSTRACT FROM AUTHOR]

In milling processes in which material removal is performed periodically from solid material, dynamic effects are generally considered to be responsible for instabilities and subsequent productivity limits. Usually, in such applications, the process-inherent complex dynamic load spectrum on machines, tools and workpieces is considered together with vibration-based relative displacements that can be attributed to the regenerative effect. There are numerous techniques in the literature addressing the suppression of these dynamic effects, but they require a large amount of analysis and implementation effort as well as specific expert knowledge. The approach presented here, however, provides a universally applicable method for suppressing chatter vibrations and deflections. By applying structure elements to the flanks of the minor cutting edges of HSS end mills, it was possible to increase the chatter-free limiting depth of cut ap,crit in the milling processes of the aluminum alloy EN AW-7075. Structured tools were used in ramp milling tests to investigate various effects, such as the influence of certain geometric design features on the stabilization potential compared to a reference tool. Furthermore, the effects of varied process parameter configurations and wear-related effects on the performance of the tool concept were focused on as well. The three key design features of the cutting edge and the structured profiles were identified from the results of the investigation, which, when combined in the most efficient design, in each case led to the development of an optimized structure and process configuration with cumulative potential for increasing the stability limit up to 200%.

A method was developed to generate diverse time series data automatically. Following the discrete Fourier transform (DFT) for time series data, the real and imaginary parts of the complex numbers in the frequency domain representation were modified using random numbers to generate new ones. Then, an inverse DFT was performed to generate virtual time series data. This innovative approach can propose diverse time series data in case studies involving actual processes and design time variation in each process variable to meet the target values of objective variables through the inverse analysis of machine learning models constructed with process data.

Kombucha is a highly nutritious beverage produced predominantly from varieties of black, oolong, and green tea using a symbiotic culture of bacteria and yeast (SCOBY). With growing kombucha markets, many extracts besides conventional tea, like herbs, fruits, milk, and agro-industrial ingredients, are being employed in the fermenting process, to produce kombucha-like-beverages. Various bioactive compounds from both substrates and metabolic activity of microorganisms involved in the fermentation are produced during processing. Thus, present review surmises the process dynamics of the kombucha fermentation process in terms of changes in the biocomponent composition, flavor profile, and microbial dynamics. Further, the advancements in the application of the kombucha process using different kinds of substrates, along with its health-benefiting bioactivities and toxicity management have been discussed. This information on kombucha fermentation consolidated in the present review would play an essential role in the food and beverage industries in exploring different substrates for the process.

A kinetic model of functioning of synapses and synaptic circuits responsible for cognitive function was developed. Cholinergic synapse functioning upon a change in the neurotransmitter concentration and acetylcholinesterase activity was analyzed in the framework of this model, and the principles of formation of a "neuroimage" and a "conduction track" in the synaptic system were explained. The effects of signal intensity, impulse frequency, receptor desensitization rate, and acetylcholinesterase expression level were analyzed. The dynamic regularities of the system behavior under constant neuron excitation (variant of stroke and action of nerve agents) were constructed. The kinetic trends upon blocking of acetylcholine receptors by physiologically active compounds and drugs were determined. The kinetic model of the synapse system was modified with involvement of elements of the mathematical theory of neural networks (artificial intelligence) with the definition of "ideal synapse" and "ideal synaptic network". [ABSTRACT FROM AUTHOR]

The present work focuses on the problem of steel surface corrosion as a kinetic expression when water droplets are repeatedly deposited and evaporated on/from its surface. This process, together with the rainwater film corrosion process, belongs to the theoretical foundations of the problem of atmospheric corrosion. It was considered that the formation of water droplets on surfaces is a random but repetitive process, as well as the fact that experimental and theoretical observations show that the droplet corrosion front of a metal surface is located in its zone circumference. We thus aimed to establish how the corrosion process evolves on a steel plate when many drops are deposited and removed repeatedly. An experimental setup and working procedure were used to obtain data characterizing the simultaneous process of steel surface corrosion and water droplet evaporation. For natural convection conditions with a variable relative humidity and temperature environment, an extensive data set consisting of the dynamics of individual droplet evaporation coupled simultaneously with the corrosion of the steel surface under the droplet was obtained. The mathematical models for evaporation and corrosion under the droplet have the same dynamic transfer surface for water evaporation and oxygen supply in the droplet. An approach for determining this surface depending on the momentary droplet mass was considered. Several simultaneous measurements of evaporation–corrosion dynamics were used to calibrate the coupled models, which were then used to show their compatibility with experimental data. [ABSTRACT FROM AUTHOR]

The paper focuses on the experimental investigation and mathematical modeling of the corrosion of steel when a film of water flows over its surface. The experimental monitoring of corrosion dynamics in the flowing film was carried out using a laboratory pilot model, exploited in such a way as to obtain data necessary to identify some characteristic parameters of the mathematical model of this problem. The mathematical model of the case takes into account the transfer of oxygen through the liquid film flowing on the surface of the corroding plate where the chemical surface processes characteristic of corrosion occur (dissolution of Fe, oxidation of Fe2+ to Fe3+, formation of surface deposit, etc.). Experimental measurements were used to identify the parameters of the mathematical model, especially the reaction constant of the Fe dissolution rate and the surface oxidation yield of Fe2+ to Fe3+. Calculation of the correlation coefficients for the apparent constant surface reaction rate and process factors showed that they correlate strongly and non-linearly with the Reynolds number (Re) of the film flow, with the cumulative flow duration, and with the cumulative standby time of the experiments. Using the dynamics of the resistance to the transfer of oxygen through the rust film and the dynamics of its thickness resulting from the specific flow of rust deposition, the apparent oxygen diffusion coefficient through the rust film formed on the plate was expressed. [ABSTRACT FROM AUTHOR]

Current literature dealing with the theory of sampling process flows is limited in scope, a situation addressed by papers presented at conferences by the author. This paper provides a summary of methods to quantify process sampling variance in a sound statistical manner. Two methods of calculation of the process sampling variance are presented here with models for robust estimation of the process covariance/variogram. The strategies for process sampling to acquire the data for process characterisation are described. The method for process characterisation can be based on special sampling campaigns, on the analysis of on-line measurement data, or on the analysis of historical data, which may be based on composite samples, such as shift samples. These scenarios are all dealt with as well as methods based on analysis of process dynamics. The latter analysis demonstrates that use of covariance functions drawn from geostatistical practice can be inappropriate for process analysis. The work is supported with plant data examples and quantitative analyses of process conditions. [ABSTRACT FROM AUTHOR]

Laser beam welding as a reliable tool for high-precision joining of batteries or microelectronics is more and more the choice for achieving reproducible results in production processes. In addition to a high automation capability, the precise control of the energy deposition into the material plays an important role, especially when highly reflective materials, such as copper or aluminum, must be welded together. Alongside the use of highly brilliant fiber lasers in the near-infrared range with a focal diameter of a few tens of micrometers, diode lasers in the wavelength range of 445 nm are increasingly being used. Here, beam diameters of a few hundred micrometers can be achieved. With a wavelength of 445 nm, the absorptivity in copper can be increased by more than a factor of 10 compared to a near-infrared laser beam sources in solid state at room temperature. This paper presents the in situ X-ray observation of laser welding processes on CuSn6 with a laser beam source with a wavelength of 445 nm using synchrotron radiation at DESY Petra III Beamline P07 EH4 in Hamburg, Germany. For the experiments, the laser radiation was focused via two separate optics to focal diameters of 362 µm and 609 µm. To characterize the dynamics of the vapor capillaries depending on the different focal diameters dF, the parameters were varied with respect to laser power PL and feed rate v. For the investigations, a synchrotron beam of 2 × 2 mm2 in size with a photon energy of 89 keV was used, and the material samples were analyzed by means of phase-contrast videography to show the boundaries between solid, liquid, and gaseous material phases. The results of this paper show the welding depths achieved and how the geometry of the vapor capillary behaves by changing the focal diameter, laser power and feed rate.

The EGFR mutation is one of the most common mutations in malignant neoplasms. The epidermal growth factor receptor (EGFR) is a growth factor receptor that induces cell differentiation and proliferation when activated by binding one of its ligands. The receptor is located on the cell surface, where ligand binding activates a tyrosine kinase in the intracellular region of the receptor. The tyrosine kinase phosphorylates a number of intracellular substrates and further activates pathways leading to cell growth, DNA synthesis and oncogene expression. Gene amplification is a process characterized by an increase in the copy number of a restricted region in the chromosome shoulder, which is associated with overexpression of the corresponding amplified gene. Amplification of the EGFR gene is detected in about 40% of glioblastoma cases. It should be noted that EGFR gene amplification is accompanied by the acquisition of many mutations, which include intragenic deletions and point mutations. The most common EGFR mutation in glioblastomas of the brain is a deletion in exon 2-7 (EGFRvIII) frame, which occurs in 50% of all cases of EGFR-amplified glioblastoma. Despite great advances in molecular biology and targeted therapies, patients with non-small cell lung cancer (NSCLC) and glioblastoma still lead in mortality. Most of them have “classical” EGFR mutations (deletions in exon 19 and 21), but 15-20% of patients have rare mutations, which most often include point mutations, deletions and insertions in exon 18 and 25. Thus, rare EGFR mutations are a promising diagnostic and therapeutic target in cancer. This review summarizes data on the role of EGFR in the carcinogenesis of NMPL and glioblastoma. The literature search was performed using the Pubmed database.

The dynamic operation of thermal power plants becomes more and more important due to more diverse and volatile power sources. Load changes of biomass, bituminous coal and methane were conducted during six experimental runs in a chemical looping combustion pilot plant which combines power generation and CO2 capture. The resulting step responses of the hydrodynamics and gas concentrations were compared to each other to understand the transient behavior of the interconnected fluidized bed system. The influence of the fuel rate and solid circulation on the response time and intensity was assessed. [ABSTRACT FROM AUTHOR]

The present work focuses on the problem of steel surface corrosion as a kinetic expression when water droplets are repeatedly deposited and evaporated on/from its surface. This process, together with the rainwater film corrosion process, belongs to the theoretical foundations of the problem of atmospheric corrosion. It was considered that the formation of water droplets on surfaces is a random but repetitive process, as well as the fact that experimental and theoretical observations show that the droplet corrosion front of a metal surface is located in its zone circumference. We thus aimed to establish how the corrosion process evolves on a steel plate when many drops are deposited and removed repeatedly. An experimental setup and working procedure were used to obtain data characterizing the simultaneous process of steel surface corrosion and water droplet evaporation. For natural convection conditions with a variable relative humidity and temperature environment, an extensive data set consisting of the dynamics of individual droplet evaporation coupled simultaneously with the corrosion of the steel surface under the droplet was obtained. The mathematical models for evaporation and corrosion under the droplet have the same dynamic transfer surface for water evaporation and oxygen supply in the droplet. An approach for determining this surface depending on the momentary droplet mass was considered. Several simultaneous measurements of evaporation–corrosion dynamics were used to calibrate the coupled models, which were then used to show their compatibility with experimental data.

Laser beam welding is a promising technology to enable automated high-quality welding procedures at significantly higher processing speeds compared to conventional processes. However, its usability is often limited by gap bridgability. This disadvantage is related to the small laser beam spot sizes that require low gap sizes for joining, which are often practically not available, and the desired welding without additional filler materials to enable high processing speeds without direction restrictions. New possibilities of beam shaping for process control are also available now for high-power laser processing and they show promising results. The resulting complex effects require additional investigation to understand the mechanisms and the use of the technologies for process improvements. Therefore, in this work, advanced beam shaping optics with up to four separate laser beam spots was used to understand the impact of multiple-spot welding on the process dynamics and gap bridgability. Gap bridgability was measured by an opening gap setup, while spatter amounts as indicators of process dynamics were measured by high-speed imaging. It was shown that multiple-spot laser welding can increase the gap bridgability, probably due to the initiated melt flow toward the joining partners. Symmetric separation of the keyholes toward the sheets increased the gap bridgability, while additional low-intensity spots in the center were able to stabilize the melt pool and reduce spattering. [ABSTRACT FROM AUTHOR]

In industrial processes, different operating conditions and ratios of ingredients are used to produce multi-grade products in the same production line. Yet, the production grade changes so quickly as the demand from customers varies from time to time. As a result, the process data collected in certain operating regions are often scarce. Process dynamics, nonlinearity, and process uncertainty increase the hardship in developing a reliable model to monitor the process status. In this paper, the source-aided variational state–space autoencoder (SA-VSSAE) is proposed. It integrates variational state–space autoencoder with the Gaussian mixture. With the additional information from the source grades, SA-VSSAE can be used for monitoring processes with sparse target data by performing information sharing to enhance the reliability of the target model. Unlike the past works which perform information sharing and modeling in a two-step procedure, the proposed model is designed for information sharing and modeling in a one-step procedure without causing information loss. In contrast to the traditional state–space model, which is linear and deterministic, the variational state–space autoencoder (VSSAE) extracts the dynamic and nonlinear features in the process variables using neural networks. Also, by taking process uncertainty into consideration, VSSAE describes the features in a probabilistic form. Probability density estimates of the residual and latent variables are given to design the monitoring indices for fault detection. A numerical example and an industrial polyvinyl chloride drying process are presented to show the advantages of the proposed method over the comparative methods. [ABSTRACT FROM AUTHOR]

The paper focuses on the experimental investigation and mathematical modeling of the corrosion of steel when a film of water flows over its surface. The experimental monitoring of corrosion dynamics in the flowing film was carried out using a laboratory pilot model, exploited in such a way as to obtain data necessary to identify some characteristic parameters of the mathematical model of this problem. The mathematical model of the case takes into account the transfer of oxygen through the liquid film flowing on the surface of the corroding plate where the chemical surface processes characteristic of corrosion occur (dissolution of Fe, oxidation of Fe2+ to Fe3+, formation of surface deposit, etc.). Experimental measurements were used to identify the parameters of the mathematical model, especially the reaction constant of the Fe dissolution rate and the surface oxidation yield of Fe2+ to Fe3+. Calculation of the correlation coefficients for the apparent constant surface reaction rate and process factors showed that they correlate strongly and non-linearly with the Reynolds number (Re) of the film flow, with the cumulative flow duration, and with the cumulative standby time of the experiments. Using the dynamics of the resistance to the transfer of oxygen through the rust film and the dynamics of its thickness resulting from the specific flow of rust deposition, the apparent oxygen diffusion coefficient through the rust film formed on the plate was expressed.

• A Slow feature network (SFNet) method is proposed for dynamics extraction and parallel linear and nonlinear monitoring. • A slow constraint is added to the autoencoder to extract both variability and velocity characteristics concurrently. • The linear and nonlinear characteristics are captured in parallel by the SFNet method. • A monitoring strategy based on SFNet is established to indicate the operating statuses with meaningful interpretation. In a typical industrial process, there may exist both linear and nonlinear relationships among process variables. Besides, the existence of process dynamics poses challenges to process monitoring. Some monitoring methods have been developed for dynamic processes. However, purely linear or nonlinear methods can hardly tackle the hybrid linear and nonlinear relationships among process variables. In this work, a novel unsupervised neural network model, named slow feature network (SFNet), is proposed to monitor dynamic processes. In SFNet, a linear mapping module and a transform gate structure are constructed to realize the parallel extraction of linear and nonlinear characteristics. Moreover, slow constraints on features are designed to capture the velocity characteristics while retaining variability information so that the process dynamic behavior can be directly observed. Various statistics are developed to monitor the process status from both variability and velocity as well as both linear and nonlinear perspectives. Thus, monitoring results corresponding to different statistical information reflect different process behavior and fault characteristics with meaningful interpretations. One numerical example and two real industrial examples are adopted to validate the proposed method. For these experimental examples, the proposed SFNet method achieves an average false alarm rate of 5.17% (min 0.8%, max 8.08%), an average detection accuracy of 87.5% (min 50.2%, max 100%), and an average precision of 98.6% (min 96.3%, max 100%), outperforming counterpart methods in the literature. [ABSTRACT FROM AUTHOR]

Planning processes guiding sustainable urban futures are typically complex, non-linear and value-laden, but their knowledge dynamics are still not adequately understood. This paper explores the potential of social network analysis (SNA) as a part of mixed-method approach for analysing the dynamic social context of knowledge integration in planning processes. The empirical study uses detailed longitudinal data of a four-year statutory planning process in the Nordic context, providing a methodological contribution for understanding knowledge integration in planning with visual–analytical methods and actor-relational criteria. Findings provide a new understanding of the actual social realities of planning practice and the further conceptualization of situated process dynamics. [ABSTRACT FROM AUTHOR]

A new dynamic model for co-ordinated scheduling of interlinked processes in a supply chain under a process modernisation is presented. Such a problem is vital in many of the supply chain management domains. This problem is represented as a special case of the scheduling problem with dynamically distributed jobs. The peculiarity of the proposed approach is the dynamic interpretation of scheduling based on a natural dynamic decomposition of the problem and its solution with the help of a modified form of continuous maximum principle blended with combinatorial optimisation. The special properties of the developed model allow using methods of discrete optimisation for the schedule calculation. Optimality and sufficiency conditions as well as structural properties of the model are investigated. Advantages and limitations of the proposed approach are discussed. With the developed approach, an explicit inclusion of a process modernisation in the SC co-ordinated decisions for a wide ranges of possible applications as well as a dynamic model and a tractable algorithm for optimal discrete time scheduling on the basis of continuous maximum principle have been obtained. [ABSTRACT FROM PUBLISHER]

For any building project, the project management triangle QCT (Quality, Cost, Time) are decisive in the decision-making process. Indeed, better, faster and cheaper remain very important goals in the design of new industrial products in a competitive environment. In this paper, we propose a reference framework to formalize the quality criterion that characterizes a building made during a project. A quality measurement is then defined based on the performance levels of the functions actually provided by a building and the requirements originally formulated by its owner. In addition, a modeling of the building production process is proposed to observe the effects of technical or managerial choices on the expected quality of a new or renovated building. Finally, a representation of the level of performance of each building technical attribute over its life cycle is proposed in order to represent the performance recovery allowed by a renovation operation as a resilience process. In the end of this paper, a section is dedicated to a computational experiment for illustrating the theoretical approach. [ABSTRACT FROM AUTHOR]

Costs for installation and maintenance of protective structures are increasing while alpine hazards progressively threaten alpine communities, infrastructure and economics. Thus, reliable process‐based anticipation and early warning strategies offer a cost‐effective and smart solution for alpine societies in the near future. However, only few comprehensive pre‐failure observations of alpine rock slopes have been reported so far. This paper demonstrates pre‐failure observations of a rapidly deforming rock mass (potentially 260,000 m3) at the Hochvogel (Allgäu Alps, 2,592 m a.s.l.) and a geotechnical monitoring and warning concept. This is implemented in the complementary multi‐method approach of the AlpSenseBench project and the basis for an effective and reliable early warning system. Since 2014, overall displacement rates in the range of 2 to 10 mm/month in the main decametre deep fracture are observed. It is expected that predictive acceleration patterns will appear in the final pre‐failure stage. A detailed knowledge of multiple anticipative signals in correlation with accelerating rock slope deformations will contribute to an advance in accuracy and reliability of rock slide early warning. [ABSTRACT FROM AUTHOR]

Process dynamics is widely presented in industrial processes, which can be perceived as temporal correlations. Negligence of dynamic information may result in misleading monitoring results. Therefore, explicit exploration of dynamic information is crucial to process monitoring. In this paper, a new data-driven algorithm called enhanced canonical variate analysis with slow feature (ECVAS) and corresponding monitoring strategy are proposed for dynamic process monitoring. First, a new objective function is defined with two goals, which attempts to extract slowly varying latent variables in addition to high temporal correlation. Hence, the latent variables called slow canonical variables (SCVs) would capture valuable dynamic information and be isolated from static information and fast-varying noises. Second, the process dynamics has been explored in detail by concurrently monitoring of temporal correlations and varying speed. Therefore, the proposed method achieves in-depth understanding of process dynamics under control actions and helps identify normal changes in operating conditions. Third, process static information and dynamic information have been separately monitored, contributing to a fine-scale identification of process variations. Finally, the validity of the proposed strategy is illustrated with an industrial scale multiphase flow experimental rig and a real thermal power process. • A new objective is proposed to isolate valuable dynamic information from data by concurrent consideration of temporal correlation and process variation speed. • A legible interpretation of process dynamics is provided by concurrently monitoring of temporal correlations and varying speed, which successfully recognizes normal changes in operating conditions. • The proposed algorithm provides separately monitoring of process dynamic and static information, which contributes to comprehensive exploration of the process variations. • The validity of the proposed method is illustrated with two industrial processes. [ABSTRACT FROM AUTHOR]

A kinetic model describing the dynamics of synaptic "discharge", taking into account the kinetics of the neurotransmitter injection into the synaptic cleft, pH dependence of the enzyme catalytic activity, and proton removal by diffusion was proposed and studied. In the framework of the kinetic model, functioning of the cholinergic synapse was considered. The results of mathematical modeling of the change in the acetylcholine level, induced pH impulse, and the effect of the impulse transmission frequency and acetylcholinesterase inhibition are presented. A physicochemical interpretation was given for a number of key important physiological phenomena, such as neuromuscular paralysis, the mechanism of information recording and storage in the neurological memory, the action of nerve poisons and toxins, and Alzheimer's disease. [ABSTRACT FROM AUTHOR]

The essence of Continuous Manufacturing (CM) resides in the fact that continuous process units are directly connected to each other forming a continuous process train. The thorough understanding of material flow in this train based on suitable sensors, including on-line process analytical technologies and other sensors, is key in understanding the time-domain behavior of the system and the process. This real-time monitoring correlated with the time domain material flow behavior could be used to close control-loops. In practical terms, the implementation of such a control strategy is only feasible, if the overlying control system knows precisely what material is when and where at all times. Consequently, thorough knowledge of the residence time distribution (RTD) of the material throughout the whole manufacturing network needs to be established early on in development. Once RTD is well understood, its constant observation could also be used for continuous process verification purposes hinging on the argument that the flow pattern of the material is unchanged. As continuous processes that run over extended periods of time are susceptible to unforeseen incidents like equipment wear-out or clogging, drifts or shifts in RTD could indicate such issues early on. The presented work aims to demonstrate this proposed concept for an integrated wet-granulation CM process. To achieve this aim, three steps were completed: First, thorough RTD knowledge was generated, by inducing endogenous step-tests in active pharmaceutical ingredient (API) content in the range of ±30% at varying process conditions, and analyzing the material RTDs via NIRS analysis at four different locations in the line. Second, it was demonstrated that also low-level step tests of ±5% and even ±3% are sufficient for accurate RTD determination. This validated the possibility of continuous RTD assessment during (pre-)validation trials or even commercial manufacturing, as the drug product would comply with required quality characteristics (content uniformity, assay). In the third step, it was then demonstrated that recurring low-level step testing during routine manufacturing could be used as a way to determine the current system health, as observed changes in RTD indicated blockages and accidental material hold-up in the line. While deliberate changes in API content during commercial production might seem counter intuitive, they would actually aid in ensuring the production of quality product in a better way, than running at constant process settings over an extended period of time without the constant assessment of system health. [ABSTRACT FROM AUTHOR]

It is the ambition of many social innovation initiatives to develop new solutions – often in the form of services – to environmental issues by locally framing often global environmental problems. These green social innovations are very diverse in nature. In order to better systematize this diversity, we first address the questions, what particular challenges do social innovations in the area of the green economy experience. Second, we want to establish a typology of social innovation initiatives that provides insights in the special challenges of social innovations in the green economy. The empirical basis for this contribution is formed by a mapping of 300 social innovation initiatives in the area of environment, energy and mobility, and expert workshops. In order to contribute to an improved understanding of the processes of social innovation and transcend the limits of the single social innovation activity, we study types of social innovations, dynamic patterns of their development and challenges specific to green social innovations. [ABSTRACT FROM AUTHOR]

Computational models introduce simplifications that need to be understood and validated. For attractor models of decision making, the main simplification is the high-level representation of different sub-processes of the complex decision system in one dynamic description of the overall process dynamics. This simplification implies that the overall process dynamics of the decision system are independent from specific values handled in different sub-processes. Here, we test the validity of this simplification empirically by investigating choice perseveration in a nonverbal, value-based decision task. Specifically, we tested whether choice perseveration occurred irrespectively of the attribute dimension as suggested by a simulation of the computational model. We find evidence supporting the validity of the simplification. We conclude that the simplification might capture mechanistic aspects of decision-making processes, and that the summation of the overall process dynamics of decision systems into one single variable is a valid approach in computational modeling. Supplement materials such as empirical data, analysis scripts, and the computational model are publicly available at the Open Science Framework (osf.io/7fb5q). [ABSTRACT FROM AUTHOR]

Assessment of operating performance for industrial processes is critical to guarantee high productivity and low cost, under routine operating condition. In the traditional research works on assessment of operating performance, the static characteristics are fully investigated, but the dynamic characteristics are seldom explored. Actually, the dynamic characteristics are important to distinguish operating performance and indicate the regulating actions of controllers. This article presents a concurrent static and dynamic assessment (ConSDA) method for operating performance in terms of industrial processes under closed-loop control. The performance levels are distinguished from both static and dynamic aspects. Canonical variate analysis and slow feature analysis are combined to fully extract the static and dynamic features of a process to well characterize each performance level. An efficient assessing scheme using the Bayesian inference based criterion is developed to provide meticulous assessing result with meaningful physical interpretability and sensitive switching identification for performance levels. The efficacy is demonstrated through application to a numerical example and a three-phase flow process. The rates of accurately distinguishing the performance levels for ConSDA is over 95% for the two applications with strong dynamic properties. Meanwhile, the highest average accuracy rates of four other assessing methods is 87.0%. The comparison illustrates the superiority of ConSDA. [ABSTRACT FROM AUTHOR]

Machining operations like milling exhibit complex process kinematics, which restricts the identification and analysis of fundamental cause-effect interrelations. In order to differentiate the impact of relevant process parameters, a reduction of influencing factors is necessary. For this purpose, a cutting operation with a linear cutting motion, like orthogonal cutting, is beneficial. Such an experimental setup requires suitable tools, workpieces and measurement systems. The experimental assessment of a developed modular design is presented in this publication. In addition to orthogonal cuts, oblique cuts, which are representative for machining operations with respect to the helix angle, were investigated. To analyze process dynamics, interrupted cuts, including a specifically compliant tool system, were performed. [ABSTRACT FROM AUTHOR]

The integration of engineering process control (EPC) and statistical process control (SPC) has drawn wide attention because of its capability to minimize long-term process variation and improve product quality. However, dynamic shift patterns are usually observed when feedback-control loops exist, which consequently lead to poor charting performance. In this paper, the dynamic patterns in mean shifts of proportional integral (PI) controlled and minimum mean square error (MMSE) controlled processes are formulated and analysed. In conjunction with a model-free forecasting algorithm, an adaptive T2 charting procedure is applied to improve detection power. This adaptive procedure is constructed upon the uniformly most powerful (UMP) unbiased test with the consideration of time-varying patterns. Monte Carlo simulations have shown that the proposed strategy possesses significantly improved detection sensitivity over intended shift ranges. [ABSTRACT FROM AUTHOR]

Introduction. Any metal-cutting machine has errors of the executive elements movement depending on its geometric accuracy and state. These errors change the forming motions indirectly, through their transformation by the dynamic cutting system with account for the elastic deformations of the tool and the workpiece. Materials and Methods. Laws of the relation between the executive elements and forming paths for the longitudinal cutting case are considered. All deformation displacements are counted off from the independent coordinate system anchored to the carrier system of the machine which is considered to be absolutely rigid and non-deforming. Research Results. A mathematical transformation model consisting of two elastic-deformation subsystems on the part of the tool and workpiece that interact through the dynamic link generated by the cutting process is proposed. This model allows for the errors of the executive elements movement. They are specified by the kinematic disturbances and spindle wavering, and a workpiece in the space. Discussion and Conclusions. Therefore, as opposed to the known studies, it is shown how the mathematical formulation and parameters of the dynamic link generated under the cutting process are changed depending on the kinematic and other types of disturbances. Two types of disturbances are fully considered: those inducing speed variations of the tool motion relative to the workpiece in the direction to the caliper mobility, and disturbances in the form of oscillating displacements in the orthogonal direction to the feed velocity attitude. These disturbances are generated as radial and axial spindle wavering, and as speed variations of the longitudinal feed and variations of the caliper position relative to the ideal rotation axis of the part. It is shown, that the law of the disturbance transformation into the forming movements trajectories depends on the frequency content of the disturbances. The examples are provided, and the conclusions about the transformation properties are made. In particular, it is shown that the speed variations in the direction to the longitudinal feed with the frequency equal to or multiple of the spindle rotation frequency in no way image into the forming movement trajectories.

• Active learning through an industrial process control problem. • Students experience and reflect on dynamic process behavior. • Outline of two process design and control course set-ups. The domains of process design, operations and control are highly interdependent and thus affect operational efficiency and robustness of industrial facilities. Despite this, they are mostly kept in isolation from each other even in commercial projects, which is probably due to the perceived increase in complexity and aligned with prior experience from university education. Process design covers mostly the steady state, whereas process control is generally taught with a strong focus on classical control theory, which can be difficult to apply in industry. A reason for not combining the three domains of process design, operations and control is the lack of simple, student friendly teaching aids that can be used within time constraints of (under-)graduate teaching. The Tennessee Eastman Process (TEP) challenge, introduced in the early 90′s, originally as a process control benchmark simulation, can be used as a teaching aid to impart practical understanding of process design, dynamics and control to students. To this end, we will discuss the unique properties of the TEP, which make it an excellent tool to introduce process dynamics and control while reinforcing understanding of fundamentals, unit operations and the complexities and consequences of combining unit operations. We will then identify key developments that are needed to make the TEP a useful tool for teaching and discuss developments that have been carried out by the authors in this respect. Subsequently, it is shown how the modified model creates learning opportunities with respect to elementary as well as advanced control techniques and design assessments. The paper concludes with an outline of how this tool is currently used in a process design course with a strong emphasis on dynamics and control and in a classical undergraduate course on process control. [ABSTRACT FROM AUTHOR]

The wetting behavior in laser brazing can be designated as inconstant, caused largely by external process discontinuities such as the wire feeding. To reveal periodic melt pool propagation effects that occur during laser brazing of aluminum and for a better understanding of those effects in laser brazing in general, this paper analyzes high-speed recordings of the brazing process with aluminum alloy. It is demonstrated that two main effects of periodic melt pool behavior in different frequency scales occur during the process, related directly to the wire feeding. [ABSTRACT FROM AUTHOR]

This study presents the frequency‐domain and state‐space approaches for modelling and identification of processes exhibiting non‐minimum phase and time delay characteristics. An asymmetrical relay is fed back to unknown process to bring sustained oscillatory responses or limit cycle at the process output. Utilising the relay settings and limit cycle information, a set of mathematical expressions for the identification of process dynamics in terms of non‐minimum phase second‐order plus time delay, non‐minimum phase first‐order plus time delay, non‐minimum phase integrating first‐order plus time delay models is deduced. As compared to the methods reported in recent literature, the proposed set of explicit expressions is completely free from the simultaneous solution of a set of non‐linear equations and does not need any prior guess for initial value of the unknown process model parameters. Simulations of benchmark examples from literature and experimental results from coupled tanks system are included to demonstrate the efficacy of the proposed relay‐based identification algorithms. [ABSTRACT FROM AUTHOR]

The authors investigate the problem of state estimation for stochastic hybrid dynamical systems with state equality constraints. They divided this problem into three categories depending on the linearity or non‐linearity of equality constraints as well as on the dependence of constraints on the operating mode. For the mode‐independent equality‐constrained linear case, they present sufficient conditions on process dynamics and filter initial conditions so that the classical interacting multiple model (IMM) algorithm yields state estimates satisfying the linear equality constraint for all subsequent times. For both linear and non‐linear systems, the mode‐dependent equality constraints must be enforced over time by the filter. They present a modified version of the IMM filter to enforce the equality constraints in such cases. Their numerical results show that the proposed methods provide more accurate estimates than the IMM unconstrained estimates. [ABSTRACT FROM AUTHOR]

Closed-loop control is commonly used in industrial processes to track setpoints or regulate process disturbances. Process dynamics resulting from closed-loop control are reflected in data mainly in two aspects, namely serial correlation and variation of response speed. Concurrent analysis of both aspects from data has not been fully investigated in the literature. In this work, a combined strategy of canonical variate analysis and slow feature analysis is proposed to monitor process dynamics resulting from closed-loop control by exploring both serial correlations and variation speed of process data. First, the canonical subspaces reflecting serial correlation are modeled by maximizing correlation between the past and future values of the process data. Then, both the serially correlated canonical subspace and its residual subspace are further explored to extract the slow features, which are representations of process variation speed. The proposed method provides a meaningful physical interpretation and in-depth process analysis with considerations of process dynamics under closed-loop control. Besides, it provides a concurrent monitoring of both process faults and operating condition deviations, resulting in fine-scale identification of different operation statuses. To demonstrate the feasibility and effectiveness, the proposed strategy is tested in a simulated typical chemical process under closed-loop control, namely the three-phase flow process. [ABSTRACT FROM AUTHOR]

• Fine grinding in fluidized bed opposed jet mills leads to product accumulation. • The determined high solid load at the classifier leads to a fish-hook effect. • Accumulation of fines is enhanced by high classifier speeds and holdups. • The time for the product to reach a steady-state increases with higher holdups. • High speed imaging of the classifier periphery reveals the formation of cluster. Dry grinding of particles to sizes below 10 µm can be realized in fluidized bed opposed jet mills. In these mills the energy for comminution is supplied by pressurized gas, which is introduced through focused nozzles. The gas transports the material towards an internal classifier, which separates the fines from the coarse material. The fines are discharged whereas the coarse material is recycled. Within this study special attention is paid to separation at the classifying wheel. Limestone batch grinding experiments were performed in a fluidized bed opposed jet mill equipped with on-line and in-line probes: The particle size distributions (PSDs) of the product flow and the solid concentration below the classifier were determined on-line. The flow field around the classifier was recorded by a high-speed camera and off-line measurements of the mill inventory and its PSD were performed. Our measurements reveal that the solid transport from the milling zone to the classifier and the classifier performance strongly depend on solid concentration. Increasing the solid feed concentration or the classifier wheel speed leads to unwanted accumulation of product-sized particles inside the mill. In particular, we find high solid loadings of up to 1.05 g⋅g−1 and strong cluster formation (local zones of high solid concentration) in the vicinity of the classifier blades. Estimated separation efficiency curves of the classifier show a strong "fish-hook effect" which increases with the solid concentration. Our findings are relevant for future process optimization by careful tuning of grinding performance, holdup and classifier speed. [ABSTRACT FROM AUTHOR]